AUTHORS: Azam Beg and S.K. Hasnain
PUBLICATION/VENUE: International Multi-Topic Conference (IMTIC'08), Apr 2008, pp. 74-78.
This paper presents a speech processing and recognition system for individually spoken Urdu language words. The speech feature extraction was based on a dataset of 150 different samples collected from 15 different speakers. The data was pre-processed using normalization and by transformation into frequency domain by (discrete Fourier transform). The speech recognition feed-forward neural models were developed in MATLAB. The models exhibited reasonably high training and testing accuracies. Details of MATLAB implementation are included in the paper for use by other researchers in this field. Our ongoing work involves use of linear predictive coding and cepstrum analysis for alternative neural models. Potential applications of the proposed system include telecommunications, multi-media, and voice-activated tele-customer services.
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